Machine Learning Interpretability

Ms. Navidi has completed her Ph.D. in AI (autonomous driving field) from École de Technologie Supérieure (ÉTS), and postdoctoral studies from HEC Montréal, McGill University, and Polytechnique Montréal. She has been a machine learning (ML) researcher, applied research scientist and data scientist in different research teams. She is also an expert in deep learning, reinforcement learning, supervised / unsupervised learning, natural language processing, computer vision, and time-series data. Neda has been invited in several talks and oral/tutorial presentations in various summits, conferences and workshops related to machine learning. She now works as AI research manager in AI research and development department at AI Redefined Inc.

Human and Multi-Agent Collaboration Framework

• Decision-making tasks;
• Human can help AI to learn faster and more reliable;
• AI can help human to learn more efficient.

Mo is a serial entrepreneur and Goldman Sachs alumni, with several degrees from Europe top 5 universities. His work has been featured in Forbes, Billboard, Retail Week and Mindshare and has won awards at Cannes Lion. Mo has spent the last decade building technology and teams across Europe, US and Asia and has done talks on AI and ML to several agencies and brands.

Natural Language Generation: Creating human readable text with machines

With the advent of GPT-3, in this talk we look at a case study based on our experience into natural language generation, where it works , where it doesn't and what to expect from the next generation of language models.

• A look back at text generation
• Where we are now and the current state of the technology
• A case study that looks where the text generation works and where it doesn't, and common pitfalls

Eline Chivot is a senior policy analyst at the Center for Data Innovation. Based in Brussels, Eline focuses on European technology policy issues and on how policymakers can promote digital innovation in the EU. Prior to joining the Center for Data Innovation, Eline worked as a policy analyst at the Hague Center for Strategic Studies (HCSS), where her work included research projects on defense, security, and economic policy issues. More recently, Eline worked at one of Brussels' largest trade associations, DigitalEurope, and managed its relations with representatives of the digital tech industry in Europe and beyond. Eline earned master's degrees in political science and economics from Sciences Po Lille, and in strategic management and business administration from the University of Lille.

"Fixing" AI with Policy: Is Explainability a Feasible Solution?

Policymakers are racing to come up with rules on the development and deployment of AI systems, based on legal and ethical principles that would be imposed on companies. One of the most discussed principle is explainability. To what extent is it feasible? What are stakeholders saying about its feasibility and impact on the development of AI?

David Curran is a machine learning engineer, entrepreneur and hardware hacker. His most recent product was a Chinese question answering system deployed in TravelSky's Beijing data center. He has built chatbots for Mercedes, Orange Bank, La Caixa, the Dubai Government and others in French, Spanish Chinese and English.

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